Abstract
This paper presents an efficient horizontal visibility directed graph similarity algorithm (HVDS) by taking the advantages of two synchronization measuring methods in graph theory: phase locking value (PLV) and visibility graph similarity (VGS). It develops a new linear horizontal visibility graph constructing algorithm, analyzes its constructing complexity, and tests its feature performance via the sleep stages identification application. Six features are extracted, separately, from HVDS, PLV and VGS as the input to a support vector machine to classify the seven sleep stages. 11,120 data segments are used for the experiments with each segment lasts 30 seconds. The training sets are selected from a single subject and the testing sets are selected from multiple subjects. 10-cross-validation is employed to evaluate the performances of the PLV, VGS and HVDS methods. The experimental results show that the PLV, VGS and HVDS algorithms produce an average classification accuracy of 72.3%, 81.5% and 82.6%, respectively. The speed of the HVDS is 39 times faster than the VGS algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Apostol, G., Creutzfeldt, O.D.: Crosscorrelation between the activity of septal units and hippocampal EEG during arousal. Brain Research 67, 65–75 (1974)
Colter, N., Shaw, J.C.: Eeg coherence analysis and field dependence. Biological Psychology 15, 215–228 (1982)
Rosenblum, M.G., Pikovsky, A.S., Kurths, J.: Phase Synchronization of Chaotic Oscillators. Physical Review Letters 76, 1804–1807 (1996)
Stam, C.J., van Dijk, B.W.: Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Phys. D 163, 236–251 (2002)
Ahmadlou, M., Adeli, H.: Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems. Physica D: Nonlinear Phenomena 241, 326–332 (2012)
He, A., Yang, X., Yang, X., Ning, X.: Phase Synchronization in Sleep Electroencephalogram. In: 2007 IEEE/ICME International Conference on Complex Medical Engineering (CME 2007), pp. 1421–1424 (2007)
Acharya, A., Kar, S., Routray, A.: Phase synchronization based weighted networks for classifying levels of fatigue and sleepiness. In: 2010 International Conference on Systems in Medicine and Biology (ICSMB), pp. 265–268 (2010)
Ferri, R., Rundo, F., Bruni, O., Terzano, M.G., Stam, C.J.: Regional scalp EEG slow-wave synchronization during sleep cyclic alternating pattern A1 subtypes. Neuroscience Letters 404, 352–357 (2006)
Ferri, R., Rundo, F., Bruni, O., Terzano, M.G., Stam, C.J.: Small-world network organization of functional connectivity of EEG slow-wave activity during sleep. Clinical Neurophysiology 118, 449–456 (2007)
Akṣahin, M., Aydın, S., Fırat, H., Eroǧul, O.: Artificial Apnea Classification with Quantitative Sleep EEG Synchronization. Journal of Medical Systems 36, 139–144 (2012)
Shao, Z.-G.: Network analysis of human heartbeat dynamics. Applied Physics Letters 96, 073703 (2010)
Dong, Z., Li, X.: Comment on Network analysis of human heartbeat dynamics. Applied Physics Letters 96, 266101–266102 (2010)
Ahmadlou, M., Adeli, H., Adeli, A.: New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. Journal of Neural Transmission 117, 1099–1109 (2010)
Yang, Y., Wang, J., Yang, H., Mang, J.: Visibility graph approach to exchange rate series. Physica A: Statistical Mechanics and its Applications 388, 4431–4437 (2009)
Elsner, J.B., Jagger, T.H., Fogarty, E.A.: Visibility network of United States hurricanes. Geophys. Res. Lett. 36, L16702 (2009)
Stam, C.J.: http://home.kpn.nl/stam7883/brainwave.html
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 21–27 (2011)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101, e215–e220 (2000)
Ronzhina, M., Janoušek, O., Kolářová, J., Nováková, M., Honzík, P., Provazník, I.: Sleep scoring using artificial neural networks. Sleep Medicine Reviews 16, 251–263 (2012)
Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring systems for sleep stages of human subjects. In: Office, U.G.P. (ed.) Public Health Service, Washington DC (1968)
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuño, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105, 4972–4975 (2008)
Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80, 046103 (2009)
Gutin, G., Mansour, T., Severini, S.: A characterization of horizontal visibility graphs and combinatorics on words. Physica A: Statistical Mechanics and its Applications 390, 2421–2428 (2011)
Chapotot, F., Becq, G.: Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules. International Journal of Adaptive Control and Signal Processing 24, 409–423 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, G., Li, Y., Wen, P.P. (2012). An Efficient Visibility Graph Similarity Algorithm and Its Application on Sleep Stages Classification. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-35139-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35138-9
Online ISBN: 978-3-642-35139-6
eBook Packages: Computer ScienceComputer Science (R0)